Overview

Food insecurity, or a “lack of consistent access to enough food for an active, healthy life”, is a major public health issue in the United States, where approximately 11% of households experienced food insecurity in 2018 (1). The United States Department of Agriculture (USDA) publishes a Food Environment Atlas, which contains data on over 275 variables with respect to food insecurity, including access to food retail, use of government food assistance programs, health and socioeconomic indicators, etc. These data are collected from various sources, span several years and geographic levels, and have been made publicly available. The goal of this project is to assess how the food retail environment varies across space, and assess the relationship between food access and other indicators such as race, income, food assistance, and health outcomes variables. Geospatial differences will be analyzed, with a specific focus on Philadelphia in comparison to 1) other counties in Pennsylvania, and 2) Baltimore, MD, another large urban county recommended as a suitable comparison by Moriah Hall, a faculty consultant.

Github Repository

Introduction

In the United States, approximately 11% of households were food insecure in 2018 (1). In Philadelphia, this rate is nearly double the national average at 18.3%, and more than one out of every five Philadelphian struggles with basic food access (2). Food insecurity contributes to the development of chronic diseases such as heart disease and diabetes, both of which are two of the United State’s top ten leading causes of death (3). Additionally, food insecurity affects children’s cognitive development, and their ability to excel in school (4). Thus, food insecurity has long-lasting impacts on the productivity of a population, and understanding the regional factors that contribute to food insecurity and adverse health outcomes is vital for creating effective local and national interventions.

A food secure environment provides both physical and financial access to nutritious food. By analyzing the food retail environment in context of socioeconmic factors and government assistance programs, this project spans the disciplines of sociology, economics, geography, public policy, and public health. Based on consulting with Dr. Alyson Karpyn, an analysis on a vulnerable but previously under-researched population, senior citizens, has also been incorporated. Another consultant, Moriah Hall, suggested doing a comparison of Philadelphia, PA to Baltimore, MD, a peer urban county. This project thus aims to use a multidisciplinary approach to understanding regional-specific factors influencing food insecurity, and in doing so, may provide critical insight on how to tackle food insecurity in the United States.

Methods

Downloading data and necessary packages

The main data set used for this project was obtained from the USDA’s publicly available data on the Food Environment Atlas. The downloaded Excel file contained several sheets pertinent to this project and have been loaded into R as a list of dataframes called “mysheets”. These dataframes contain county-specific information on access and proximity to food retail providers (grocery stores, convenience stores, farmers’ markets, etc), food assistance usage, food insecurity rates, diabetes and obesity rates, and socioeconomic indicators including race, age, income, etc.

To create maps, the counties polygon dataset from BMIN503 Assignment 5 has been downloaded and used.

All necessary packages will be installed and loaded first.

## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.2.1     ✔ purrr   0.3.3
## ✔ tibble  2.1.3     ✔ dplyr   0.8.3
## ✔ tidyr   1.0.0     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0
## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0

The data downloaded has been saved into the working directory and is loaded into R. Note that names of some of the sheets were slightly modified in Excel to exclude spaces and make them R-compatible.

## # A tibble: 6 x 44
##   FIPS  State County LACCESS_POP10 LACCESS_POP15 PCH_LACCESS_POP… PCT_LACCESS_POP… PCT_LACCESS_POP… LACCESS_LOWI10 LACCESS_LOWI15 PCH_LACCESS_LOW… PCT_LACCESS_LOW… PCT_LACCESS_LOW… LACCESS_HHNV10 LACCESS_HHNV15 PCH_LACCESS_HHN… PCT_LACCESS_HHN… PCT_LACCESS_HHN… LACCESS_SNAP15 PCT_LACCESS_SNA… LACCESS_CHILD10 LACCESS_CHILD15 LACCESS_CHILD_1… PCT_LACCESS_CHI… PCT_LACCESS_CHI… LACCESS_SENIORS…
##   <chr> <chr> <chr>          <dbl>         <dbl>            <dbl>            <dbl>            <dbl>          <dbl>          <dbl>            <dbl>            <dbl>            <dbl>          <dbl>          <dbl>            <dbl>            <dbl>            <dbl>          <dbl>            <dbl>           <dbl>           <dbl>            <dbl>            <dbl>            <dbl>            <dbl>
## 1 01001 AL    Autau…        18428.        17497.            -5.06            33.8             32.1           5344.          6544.           22.4               9.79            12.0            664.           678.             2.03             3.28             3.35          932.             4.61            4823.           4617.            -4.26            8.84              8.46            2388.
## 2 01003 AL    Baldw…        35211.        30561.           -13.2             19.3             16.8           9952.          9887.           -0.656             5.46             5.42          1572.          1394.           -11.3              2.15             1.91          951.             1.30            7916.           7008.           -11.5             4.34              3.84            6404.
## 3 01005 AL    Barbo…         5722.         6070.             6.07            20.8             22.1           3136.          2949.           -5.96             11.4             10.7            406.           425.             4.68             4.14             4.33          423.             4.30             940.           1032.             9.73            3.43              3.76             770.
## 4 01007 AL    Bibb           1045.          969.            -7.22             4.56             4.23           491.           596.           21.3               2.14             2.60           275.           224.           -18.4              3.46             2.82           53.8            0.677            249.            233.            -6.65            1.09              1.02             151.
## 5 01009 AL    Blount         1548.         3724.           141.               2.70             6.50           609.          1651.          171.                1.06             2.88           705.           720.             2.05             3.27             3.34          175.             0.813            385.            911.           137.              0.671             1.59             195.
## 6 01011 AL    Bullo…         4090.         4142.             1.27            37.5             38.0           2199.          2155.           -2.03             20.2             19.7            273.           416.            52.4              7.29            11.1           225.             6.02             607.            617.             1.58            5.56              5.65             554.
## # … with 18 more variables: LACCESS_SENIORS15 <dbl>, PCH_LACCESS_SENIORS_10_15 <dbl>, PCT_LACCESS_SENIORS10 <dbl>, PCT_LACCESS_SENIORS15 <dbl>, LACCESS_WHITE15 <dbl>, PCT_LACCESS_WHITE15 <dbl>, LACCESS_BLACK15 <dbl>, PCT_LACCESS_BLACK15 <dbl>, LACCESS_HISP15 <dbl>, PCT_LACCESS_HISP15 <dbl>, LACCESS_NHASIAN15 <dbl>, PCT_LACCESS_NHASIAN15 <dbl>, LACCESS_NHNA15 <dbl>, PCT_LACCESS_NHNA15 <dbl>,
## #   LACCESS_NHPI15 <dbl>, PCT_LACCESS_NHPI15 <dbl>, LACCESS_MULTIR15 <dbl>, PCT_LACCESS_MULTIR15 <dbl>

Load in the counties polygon dataset to use for creating maps.

The following themes will be used throughout the report for generating maps.

Cleaning data

The next step is to clean the data using the dplyr and tidyverse packages. Each dataframe will be updated to only include variables that will be analyzed in this project.

library(dplyr)
Access <- mysheets$ACCESS %>%
  select("FIPS",    "State",    "County", "PCH_LACCESS_POP_10_15",
"PCT_LACCESS_POP10",
"PCT_LACCESS_POP15", "PCH_LACCESS_LOWI_10_15",
"PCT_LACCESS_LOWI10",
"PCT_LACCESS_LOWI15", "PCH_LACCESS_HHNV_10_15",
"PCT_LACCESS_HHNV10",
"PCT_LACCESS_HHNV15", "PCT_LACCESS_SNAP15",
"PCT_LACCESS_CHILD10",
"PCT_LACCESS_CHILD15", 
PCH_LACCESS_SENIORS_10_15,
PCT_LACCESS_SENIORS10,
PCT_LACCESS_SENIORS15,
"PCT_LACCESS_WHITE15",
"PCT_LACCESS_BLACK15",
"PCT_LACCESS_HISP15",
"PCT_LACCESS_NHASIAN15",
"PCT_LACCESS_NHNA15",
"PCT_LACCESS_NHPI15",
"PCT_LACCESS_MULTIR15")
  
Stores <- mysheets$STORES %>% #Turns out I don't need to add "" for the column names!
  select(FIPS,  State,  County, GROCPTH09,
GROCPTH14,
PCH_GROCPTH_09_14,
SUPERCPTH09,
SUPERCPTH14,
PCH_SUPERCPTH_09_14,
CONVSPTH09,
CONVSPTH14,
PCH_CONVSPTH_09_14,
SPECSPTH09,
SPECSPTH14,
PCH_SPECSPTH_09_14,
SNAPSPTH12,
SNAPSPTH16,
PCH_SNAPSPTH_12_16,
WICSPTH08,
WICSPTH12,
PCH_WICSPTH_08_12)

Restaurants <- mysheets$RESTAURANTS %>% 
  select(FIPS,  State,  County,
  FFRPTH09,
FFRPTH14,
PCH_FFRPTH_09_14,
FSRPTH09,
FSRPTH14,
PCH_FSRPTH_09_14)

Assistance <- mysheets$ASSISTANCE %>% 
  select(FIPS,  State,  County,
  REDEMP_SNAPS12,
REDEMP_SNAPS16,
PCH_REDEMP_SNAPS_12_16,
PCT_SNAP12,
PCT_SNAP16,
PCH_SNAP_12_16,
SNAP_PART_RATE08,
SNAP_PART_RATE13,
PCT_NSLP09,
PCT_NSLP15,
PCH_NSLP_09_15,
PCT_FREE_LUNCH09,
PCT_FREE_LUNCH14,
PCT_REDUCED_LUNCH09,
PCT_REDUCED_LUNCH14,
PCT_SBP09,
PCT_SBP15,
PCH_SBP_09_15,
PCT_SFSP09,
PCT_SFSP15,
PCH_SFSP_09_15,
REDEMP_WICS08,
REDEMP_WICS12,
PCH_REDEMP_WICS_08_12,
PCT_WIC09,
PCT_WIC15,
PCH_WIC_09_15,
PCT_CACFP09,
PCT_CACFP15,
PCH_CACFP_09_15)

Insecurity <- mysheets$INSECURITY

Price <- mysheets$PRICES_TAXES %>%
  select(FIPS, State, County, MILK_PRICE10)

Local <- mysheets$LOCAL %>% 
  select(FIPS,  State,  County,
  FMRKTPTH09,
FMRKTPTH16,
PCH_FMRKTPTH_09_16,
PCT_FMRKT_SNAP16,
PCT_FMRKT_WIC16,
PCT_FMRKT_WICCASH16,
PCT_FMRKT_SFMNP16)

Health <- mysheets$HEALTH %>% 
  select(FIPS,  State,  County,
  PCT_DIABETES_ADULTS08,
PCT_DIABETES_ADULTS13,
PCT_OBESE_ADULTS08,
PCT_OBESE_ADULTS13)

SE <- mysheets$SOCIOECONOMIC %>% 
  select(FIPS,  State,  County,
  PCT_NHWHITE10,
PCT_NHBLACK10,
PCT_HISP10,
PCT_NHASIAN10,
PCT_NHNA10,
PCT_NHPI10,
PCT_65OLDER10,
PCT_18YOUNGER10,
MEDHHINC15,
POVRATE15,
PERPOV10,
CHILDPOVRATE15,
PERCHLDPOV10)

Further analyses will be conducted in the Results section.

Results

To address food insecurity, interventions need to address three main facets: 1) availability of food; 2) access to food; and 3) utilization of food. Availability of healthy, nutritious food is impacted by the physical food retail environment of a neighborhood. Are there grocery stores, farmers’ markets, or other avenues through which residents can acquire food? Access refers to both physical availability of food, but also the financial ability to acquire the food. How expensive are basic food items? Are there stores that accept government assistance benefits such as SNAP or WIC that individuals can use to purchase food? Utilization of food refers to individuals actually purchasing and consuming healthy food items. Although this dataset does not have any comsumption data, we can use redemption of SNAP and WIC benefits as a proxy for utilization, as these food assistance programs have restrictions on the types of food individuals can purchase with them. The next few sections will assess some of these questions and issues.

Lay of the land: state of food insecurity in the United States

The map below shows the three-year household food insecurity average for each state from 2013-2015. What immediately jumps out is that most states in the US have fairly high insecurity rates of above 10%. The situation is particularly dire the southern United States with rates generally being 15% or higher, with a few exceptions. States with particularly high food insecurity include: OR, AZ, LA, AL, MO, KY.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector
Food insecurity: Philadelphia, PA versus Baltimore, MD

One limitation of the three-year food insecurity rates data is that it is at the state level, and we cannot compare Philadelphia to other counties within or outside of PA. However, we can compare the state of Pennsylvania and Maryland. The bar graph below shows that the three-year average in PA and MD are very similar, making them ideal for drawing comparisons across other variables. While the food insecurity rate decreased in Maryland over time, the rate did not change in PA, and it will be interesting to explore other differences between these two states, and specifically between Philadelphia, PA and Baltimore, MD throughout this report.

Availability of food

In this section, we will explore the food retail environment across the United States. We are using food retail providers such as grocery storesas a proxy to assess availability of food.

Availability of food across the United States

The maps below depict the number of grocery stores, supercenters and club stores, convenience stores, and farmers’ markets at the county level relative to the population.

Grocery stores

For the purposes of this dataset, grocery stores included supermarkets, smaller grocery stores, and deli stores. Most counties across the United States have 0.5 or fewer grocery stores per 1000 individuals. Generally, the northern half of the US has more counties with higher numbers of grocery stores. This may be one of the factors that contribute to the lower food insecurity rate in the northern half of the United States.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

Looking specifically at Pennsylvania, Philadelphia has a higher number of grocery stores relative to other counties in Pennsylvania. This may be due to how urban Philadelphia is relative to the other counties in PA.

PA-specific Grocery Stores

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

Supercenters and club stores

Generally, the eastern half of the US appears to have higher numbers of supercenters and club stores relative to the western half.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

Relative to other counties in Pennsylvania, there are relatively few superstores and club centers in Philadelphia. Again, this may be due to how urban Philadelphia is. Rural counties may have the larger land scapes required for large warehouse stores such as superstores and club centers.

PA-specific Supercenters/club stores

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

Convenience stores

Other than a few counties that are exceptions, most of the US is relatively uniform in distribution with respect to the number of ceonvenience stores.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

There are relatively fewer convenience stores in Eastern Pennslyvania compared to western Pennsylvania. I would have expected urban areas like Philadelphia to have a higher density of convenience stores than rural counties, so this finding is a bit surprising.

PA-specific convenience stores

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector
Farmers’ markets

The northwestern half of the US has relatively higher numbers of farmers’ markets. This may influence the relatively lower rates of food insecurity in these counties.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

In Pennsylvania, the counties immediately surrounding Philadelphia have fewer farmers’ markets. This may be due to the efforts of The Food Trust and other non-profit organizations, as well as the Department of Public Health, in promoting interventions involving farmers’ markets in Philadelphia.

PA-specific farmers’ markets

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector
Comparison of Food Retail Environment: Philadelphia, PA versus Baltimore, MD

With the exception of grocery stores, Baltimore has a larger presence of stores serving food than Philadelphia relative to the population. This may account for the lower food insecurity rate observed in Baltimore, MD. However, Philadelphia has nearly double the number of grocery stores as Baltimore relative to population. This may highlight the important of not only providing availability of food, but also phhysical and financial access to food. Philadelphia’s grocery stores may be less physically accessible, or more expensive than Baltimore’s. We will explore these questions in the subsequence sections. (Note: I have not included farmers’ market in this graph because the data are from 2016, whereas the data for the other retail stores are from 2014.)

Access to food

In this section, we will explore physical and financial access to food.

Physical access to food

The dataset contains information about the number of people with low access to food. Low access in this study is defined as living more than 1 mile from a supermarket, supercenter, or large grocery store if in an urban area, or more than 10 miles if in a rural area. It also contains information about factors that might affect low access, such as income, age, and race.

Low Access to Stores

The map below shows the percent popluation with low access to stores in 2015 across the US. The western United States has higher percentage of individuals with low access to stores. What’s intriguing is that areas with low access to stores do not all overlap with areas of high food insecurity. For example, the northwestern part of the US has relatively lower food insecurity rates, yet there are many counties in this region with a high percentage of the population having low access to grocery stores. One limitation of this Access dataset is that only large grocery stores and supermarkets were considered, and many individuals may use other retail options, such as convenience stores, to meet their regular grocery needs.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

Counties immediately surrounding Philadelphia have higher percentage of population with low access to stores. In fact, relative to the rest of the state, Philadelphia has a lower percentage of individuals with low access to stores.

PA-specific low access

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector
Low Access to Stores and Low Income

The map below displays the percent population with low access to stores that also had low income in 2015. This map tracks similarly to the map for low access, as does the map specifically for PA.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

PA-specific low access to stores and low income

Low Access and Age: Philadephia, PA versus Baltimore, MD

Surprisingly, although the overall food insecurity rate in Baltimore is lower than in Philadelphia, the percentage of individuals with low access to stores is much higher in Baltimore than in Philadelphia. In Philadelphia, seniors have slightly better access than children to stores, whereas the opposite holds true for Baltimore.

Low Access and Race: Philadephia, PA versus Baltimore, MD

Philadelphia and Baltimore do not vary very much in terms of racial differences in access to stores.

#Create dataframe of interested variables
Access_race_PHLBAL <- Access %>%
  select(FIPS, County, PCT_LACCESS_POP15, PCT_LACCESS_WHITE15,
PCT_LACCESS_BLACK15,
PCT_LACCESS_HISP15,
PCT_LACCESS_NHASIAN15,
PCT_LACCESS_NHNA15,
PCT_LACCESS_NHPI15,
PCT_LACCESS_MULTIR15) %>%
  filter(FIPS %in% c("42101", "24005")) %>%
  rename(Overall = PCT_LACCESS_POP15, White = PCT_LACCESS_WHITE15, Black = PCT_LACCESS_BLACK15, Hispanic = PCT_LACCESS_HISP15,
Asian = PCT_LACCESS_NHASIAN15,
American_Indian_Alaskan = PCT_LACCESS_NHNA15,
Hawaiian_Pacific_Islander = PCT_LACCESS_NHPI15,
Multiracial = PCT_LACCESS_MULTIR15) %>%
  select(-FIPS)

ARPB <- data.frame(county = rep(c("Baltimore", "Philadelphia"), 8),
                   Race = rep(c("Overall", "White", "Black", "Hispanic", "Asian", "American_Indian_Alaskan", "Hawaiian_Pacific_Islander", "Multiracial"), each=2),
                   Percent = c(Access_race_PHLBAL$Overall, Access_race_PHLBAL$White, Access_race_PHLBAL$Black, Access_race_PHLBAL$Hispanic, Access_race_PHLBAL$Asian, Access_race_PHLBAL$American_Indian_Alaskan, Access_race_PHLBAL$Hawaiian_Pacific_Islander, Access_race_PHLBAL$Multiracial))

#Create a bar graph to visualize
ARPBplot <- ggplot(data = ARPB, aes(x=Race, y=Percent, fill = county)) + #Load data, specify variables
    geom_bar(stat="identity", position = "dodge") + #Add a visual layer that is a barplot
  scale_x_discrete(limits=c("Overall", "White", "Black", "Hispanic", "Asian", "American_Indian_Alaskan", "Hawaiian_Pacific_Islander", "Multiracial")) + #To manually adjust the order of the bars on the graph
  labs(title="Relationship Between Race and Access to Stores in Philadelphia and Baltimore 2015") + labs(x="Race", y="Percent (%) Population with Low Access to Stores") +
  theme(axis.text.x=element_text(angle=45,hjust=0.5,vjust=0.5))
ggsave(plot=ARPBplot,  filename=paste("/Users/nawarnaseer/BMIN503_Final_Project/Plot_Images/", "ARPBplot.png", sep=""), width=7, height=5, units="in")
Financial access to food

We will look at price of a basic food item, milk, across the United States as an indirect measure of affordability of food, and thus financial access. The dataset also contains information about food assistance programs such as the Supplemental Nutrition Assistance Program (SNAP) and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). We will use SNAP- and WIC- authorized stores as another indirect measure of financial access to food.

Price of milk

The map below shows the price of low-fat milk as a ratio of the national average in 2010. Ratios greater than 1 indicate that the price is higher than the national average, while ratios lower than 1 indicate that the price is lower. This data is only available at the regional level. The price is higher in the southern and eastern parts of the US. The high price in the southern US may contribute to the high food insecurity in this region. The limitation of this dataset is that it does not include other commodities, and the price of low-fat milk is not sufficient to extrapolate prices of other goods.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector
SNAP-authorized stores
## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

In Pennsylvania, Philadelphia has the highest number of SNAP-authorized stores relative to the population.

PA-specific SNAP-authorized stores

WIC-authorized stores

There is a greater density of WIC-authorized stores in counties in the northern US.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

In Pennsylvania, Philadelphia has the highest number of WIC-authorized stores relative to the population.

PA-specific WIC-authorized stores

SNAP- and WIC-authorized Stores: Philadelphia, PA versus Baltimore, MD

Philadelphia has far higher numbers of SNAP- and WIC-authorized stores compared to Baltimore.

Utilization of food

This dataset does not contain any consumption data. However, we can use the number of SNAP/WIC participants and redemption of SNAP/WIC benefits as a measure of utilization of these benefits. This can then serve as an indirect measure for consumption of food.

SNAP Participants

The map below shows the percentage of eligible individuals who participate in SNAP at the state level. Generally, there appears to be higher participation in the eastern United States. There does not appear to be a large difference between Maryland (90%) and Pennsylvania (90.4%).

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector
SNAP Redemption

Overall, there is higher redemption of SNAP benefits in the western United States, the opposite of where there are higher number of participants. There is not much of a difference in redemption between Baltimore (approximately $250,000) and Philadelphia (approximately $360,000).

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

In Pennsylvania, Philadephia has high utilization of SNAP.

PA-specific SNAP redemption

WIC Partipants Redemption

The map below shows the percentage of individuals who participate in WIC at the state level. California and Texas stand out as having more participants than any other state. However, it should be noted that this data is not a percentage of the eligible population, but the population of the state as a whole. WIC is specifically for women, infants, and children, and there may simply be more eligible individuals in California and Texas relative to other states.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector
WIC Redemption

It is difficult to make any interpretations or conclusions due to the high number of missing values for this dataset.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

WIC utilization is not as high in Philadelphia relative to its immediate surrounding counties.

PA-specific WIC redemption

There is a significant difference in WIC redemption between Baltimore and Philadelphia. However, this could be due to differences in the percentage of eligible population in these two counties, which we do not have data on.

Health outcomes

Food insecurity can have adverse effects on various health outcomes. In this section, we explore spatial distribution of obesity and diabetes to assess in overlap with the distribution of food insecurity.

Diabetes

Diabetes is more prevalent in the southeastern part of the US, which does overlap with areas of high food insecurity.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

In Pennsylvania, prevalence of diabetets is higher in Philadelphia relative to surrounding counties.

PA-specific diabetes

Obesity

Obesity is more prevalent in the eastern part of the US.

## Warning: Column `GEO_ID` joining factor and character vector, coercing into character vector

Prevalence of obesity higher in Philadelphia relative to surrounding counties.

PA-specific obesity

Conclusion

The maps generated in this report provide useful insight into spatial differences in factors influencing food insecurity throughout the United States. While this report focused on certain geographical areas (e.g. Philadelphia, PA and Baltimore, MD), the analyses generated in this report can be further studied and applied to other states and counties as well. The USDA Food Atlas also contains several other variables and indicators that were not incorporated into this report, but that would be useful to investigate in future studies. While the Food Atlas is very comprehensive in terms of variables, one limitation is that not all variables have data available at the county level. Additionally, not all variables have data collected from the same year. As such, comparisons of different variables across time was not possible. It would be interesting to incorporate data from other sources (e.g. NHANES, CDC datasets, etc.) for future analyses. Additionally, this report focused exclusively on the United States. Comparison of food insecurity in other developed countries may provide useful information on how best to design effective interventions.

References

  1. Coleman-Jensen, A., Rabbitt, M. P., Gregory, C. A., & Singh, A. (2019, September). Household Food Security in the United States in 2018. Retrieved November 6, 2019, from https://www.ers.usda.gov/publications/pub-details/?pubid=94848.
  2. Hunger Free America. Philadelphia Falling Behind in Freedom to Eat: Greater Philadelphia Hunger Report, 2018.
  3. Centers for Disease Control and Prevention, Leading Causes of Death, https://www.cdc.gov/nchs/fastats/leading-causes-of-death.htm
  4. Feeding America. Help Kids Facing Hunger This School Year, https://www.feedingamerica.org/hunger-blog/help-kids-facing-hunger-this